Water power is one of the key renewable energy resources,whose efficiency is often hampered due to inefficient water flow management,turbine performance,and environmental variations.Most existing optimization techniqu...Water power is one of the key renewable energy resources,whose efficiency is often hampered due to inefficient water flow management,turbine performance,and environmental variations.Most existing optimization techniques lack the real-time adaptability to sufficiently allocate resources in terms of location and time.Hence,a novel Scalable Tas-manian Devil Optimization(STDO)algorithm is introduced to optimize hydropower generation for maximum power efficiency.Using the STDO to model important system characteristics including water flow,turbine changes,and energy conversion efficiency is part of the process.In the final analysis,optimizing these settings in would help reduce inefficiencies and maximize power generation output.Following that,simulations based on actual hydroelectric data are used to analyze the algorithm's effectiveness.The simulation results provide evidence that the STDO algorithm can enhance hydropower plant efficiency tremendously translating to considerable energy output augmentation compared to conven-tional optimization methods.STDO achieves the reliability(92.5),resiliency(74.3),and reduced vulnerability(9.3).To guarantee increased efficiency towards ecologically friendly power generation,the STDO algorithm may thus offer efficient resource optimization for hydropower.A clear route is made available for expanding the efficiency of current hydropower facilities while tackling the long-term objectives of reducing the environmental impact and increasing the energy output of energy produced from renewable sources.展开更多
Cholangiocarcinoma,a form of liver bile duct cancer,is challenging to detect due to its critically low 5-year survival rate.Conventional imaging modalities,such as Computed Tomography(CT)and Magnetic Resonance Imaging...Cholangiocarcinoma,a form of liver bile duct cancer,is challenging to detect due to its critically low 5-year survival rate.Conventional imaging modalities,such as Computed Tomography(CT)and Magnetic Resonance Imaging(MRI),are widely used,but recent advancements in Hyperspectral Imaging(HSI)offer a promising,noninvasive alternative for cancer diagnosis.However,supervised learning methods often require large annotated datasets that can be difficult to obtain.To alleviate this limitation,we propose an unsupervised learning strategy using Generative Adversarial Networks(GANs)for cholangio.carcinoma detection.This approach,named Unsupervised Spectral and Spatial Attention-based GAN(USSGAN),employs an unsupervised Spectral-Spatialattention-based GAN to classify and segment cancerous regions without relying on labeled training data.The integration of an adaptive step size into Tasmanian Devil Optimization(TDO)enhances the convergence speed and effectively captures diverse cancerous features.Enhanced Tasmanian Devil Optimization(ETDO)further improves segmentation performance,making the framework robust and computationally efficient.The proposed method was tested on a publicly available multidimensional choledochal cholangiocarcino madataset,achieving superior performance compared with existing techniques in the literature.USSGAN demonstrated high accuracy across key metrics such as overall accuracy(OA),average accuracy(AA),and Cohen's Kappa.Ablation studies confirmed the critical contributions of the proposed enhancements to the overall performance.With an overall accuracy of 98.03%,the USSGAN closely aligns with the assessments of experienced pathologists while maintaining minimal computational requirements.Its lightweight nature ensures real-time deployment,providing results within a minute,making it a practical and effective solution for clinical applications.展开更多
文摘Water power is one of the key renewable energy resources,whose efficiency is often hampered due to inefficient water flow management,turbine performance,and environmental variations.Most existing optimization techniques lack the real-time adaptability to sufficiently allocate resources in terms of location and time.Hence,a novel Scalable Tas-manian Devil Optimization(STDO)algorithm is introduced to optimize hydropower generation for maximum power efficiency.Using the STDO to model important system characteristics including water flow,turbine changes,and energy conversion efficiency is part of the process.In the final analysis,optimizing these settings in would help reduce inefficiencies and maximize power generation output.Following that,simulations based on actual hydroelectric data are used to analyze the algorithm's effectiveness.The simulation results provide evidence that the STDO algorithm can enhance hydropower plant efficiency tremendously translating to considerable energy output augmentation compared to conven-tional optimization methods.STDO achieves the reliability(92.5),resiliency(74.3),and reduced vulnerability(9.3).To guarantee increased efficiency towards ecologically friendly power generation,the STDO algorithm may thus offer efficient resource optimization for hydropower.A clear route is made available for expanding the efficiency of current hydropower facilities while tackling the long-term objectives of reducing the environmental impact and increasing the energy output of energy produced from renewable sources.
基金acknowledge Princess Nourah Bint Abdulrahman University Researchers Supporting Project number (PNURSP2025R137), Princess Nourah bint Abdulrahman University, Riyadh, Saudi ArabiaProf. Qingli Li and their research group at East China Normal University, Shanghai, China, for providing access to their dataset。
文摘Cholangiocarcinoma,a form of liver bile duct cancer,is challenging to detect due to its critically low 5-year survival rate.Conventional imaging modalities,such as Computed Tomography(CT)and Magnetic Resonance Imaging(MRI),are widely used,but recent advancements in Hyperspectral Imaging(HSI)offer a promising,noninvasive alternative for cancer diagnosis.However,supervised learning methods often require large annotated datasets that can be difficult to obtain.To alleviate this limitation,we propose an unsupervised learning strategy using Generative Adversarial Networks(GANs)for cholangio.carcinoma detection.This approach,named Unsupervised Spectral and Spatial Attention-based GAN(USSGAN),employs an unsupervised Spectral-Spatialattention-based GAN to classify and segment cancerous regions without relying on labeled training data.The integration of an adaptive step size into Tasmanian Devil Optimization(TDO)enhances the convergence speed and effectively captures diverse cancerous features.Enhanced Tasmanian Devil Optimization(ETDO)further improves segmentation performance,making the framework robust and computationally efficient.The proposed method was tested on a publicly available multidimensional choledochal cholangiocarcino madataset,achieving superior performance compared with existing techniques in the literature.USSGAN demonstrated high accuracy across key metrics such as overall accuracy(OA),average accuracy(AA),and Cohen's Kappa.Ablation studies confirmed the critical contributions of the proposed enhancements to the overall performance.With an overall accuracy of 98.03%,the USSGAN closely aligns with the assessments of experienced pathologists while maintaining minimal computational requirements.Its lightweight nature ensures real-time deployment,providing results within a minute,making it a practical and effective solution for clinical applications.
